1. Field of the Invention
The disclosure relates generally to using data processing systems for cluster resource management and, in particular, to managing allocations of resources of clusters for events. Still more particularly, the present disclosure relates to using social data associated with events to improve the allocations of resources of clusters for events.
2. Description of the Related Art
As today's business needs are becoming more reliant on computer based hardware and software solutions, the need for having fast, reliable, and low cost solutions is more prevalent. Companies may use a portion of resources of clusters of computers to host their solutions. Using clusters to host solutions allows the companies to handle increased global traffic while also providing failover to prevent loss of service. Scheduled events may increase user demand for these hosted solutions.
One way to manage availability of resources of clusters while also keeping costs down is to scale a number of nodes in a cluster based on user demand. Current clustering products allow system administrators to define conditions that trigger either an increase or decrease in the number of nodes in a cluster. These conditions for changing the number of nodes in a cluster may be based on a time of day, week, month, or other schedule. However, allocating resources based on a schedule is at best an educated guess based on past performance, not current conditions. There is no guarantee that what has happened in the past will stay true for future events. This method also cannot take into account special events that may skew demand for resources or cause unexpected spikes or drops in demand.
Conditions for triggering a change in the number of nodes in a cluster may also be based on a current utilization of cluster resources. However, conditional changes based on machine resource use are reactive and thus may result in unsatisfying solution performance. For example, for solutions that can trigger quick increases in user participation, such as at the start of a meeting. In this example, there may not be enough time to reactively increase the number of nodes in a cluster, and thus the users may see response degradation.
Social networks have significantly enhanced information sharing over the Internet. Social networking sites, such as Facebook® and Twitter provide collaboration tools which allow users to interact with each other by exchanging messages with other computer users. Typical collaboration tools include tools for chatting, texting, instant messaging, multimedia messaging, emailing, conferencing, tweeting, and commenting.
Through the use of social networks users sometimes express interest and non-interest in events. The expression of interest may be for a particular event, for events of a particular type, and for topics associated with an event. For example, the expression of interest may be formed as a like or dislike. Using natural language processing, computational linguistics, and text analytics a computer system can mine source material of social networks to derive expressions of interest about events. Currently however, there is no system or process available for using expressions of interests in events in social networks to predicatively scale resources of clusters for use by solutions association with the events.
Therefore, it would be advantageous to have a method, data processing system, and computer program product that takes into account at least some of the issues discussed above, as well as possibly other issues.